@hyperfrontend/random-generator-utils

Statistical random distributions and UUID generation for simulations, testing, and procedural content.

What is @hyperfrontend/random-generator-utils?

@hyperfrontend/random-generator-utils provides random number generators beyond JavaScript's basic Math.random(), focusing on statistical distributions used in simulations, load testing, and procedural generation. It includes Gaussian (normal), exponential, power law, and logarithmic distributions, plus UUID v4 generation and seeded pseudo-random functions.

Unlike cryptographic random generators (like Web Crypto API), these utilities prioritize reproducibility and distribution shapes over security. The seeded pseudo-random generator allows deterministic sequences for testing, while statistical distributions model real-world phenomena like response times, user behavior, and natural variation.

Key Features

• Statistical distributions: Gaussian, exponential, power law, logarithmic, uniform
• UUID v4 generation with validation (uuidV4(), isUuidV4())
• Seeded pseudo-random for reproducible sequences in tests
• Time-based seeding for pseudo-random variations
• Zero dependencies (except sibling @hyperfrontend/data-utils)
• Pure functions for functional composition

Architecture Highlights

All generators use Math.random() as the entropy source, transformed mathematically to match target distributions. Gaussian uses Box-Muller transform, exponential uses inverse transform sampling. Seeded generator uses sine function for deterministic output.

Why Use @hyperfrontend/random-generator-utils?

Realistic Load Testing and Simulations

Math.random() generates uniform distributions, but real-world events follow different patterns. User response times cluster around an average (Gaussian), server failures often show exponential decay, and popularity follows power law distributions (80/20 rule). These generators let you model realistic scenarios in load tests and simulations.

Reproducible Pseudo-Random Sequences for Testing

The seeded pseudo-random generator (randomPseudo()) produces deterministic output from a numeric seed. This enables reproducible test scenarios, snapshot testing with "random" data, and debugging flaky tests caused by true randomness. Time-based seeding (randomPseudoTimeBased()) provides daily or hourly variations while maintaining reproducibility within those windows.

UUID Generation Without External Dependencies

Many projects pull in the uuid package (500KB+) just for v4 UUIDs. This library provides a lightweight alternative with both generation and validation. Ideal for test fixtures, trace IDs, or non-security-critical unique identifiers without bloating bundles.

Functional Composition for Data Pipelines

All generators are pure functions accepting parameters and returning numbers. This makes them composable in data generation pipelines, Array methods (Array.from({ length: 100 }, () => randomGaussian(0, 100))), or streaming data generators for charts and visualizations.

Installation

npm install @hyperfrontend/random-generator-utils

Quick Start

import {
  randomGaussian,
  randomExponential,
  randomPowerLaw,
  randomUniform,
  randomPseudo,
  uuidV4,
  isUuidV4,
} from '@hyperfrontend/random-generator-utils'

// Gaussian (normal) distribution - ideal for modeling natural variation
const responseTime = randomGaussian(100, 300) // ms, centered around 200ms
const userHeight = randomGaussian(160, 180) // cm, most values near 170cm

// Exponential distribution - models time between independent events
const timeBetweenRequests = randomExponential(0.5) // λ=0.5, mean=2 seconds
const failureRate = randomExponential(0.1) // λ=0.1, mean=10 units

// Power law distribution - models "rich get richer" phenomena
const popularity = randomPowerLaw(2, 1, 1000) // Few items very popular
const citySize = randomPowerLaw(2.5, 100, 1000000) // Zipf's law for cities

// Uniform distribution - flat probability across range
const randomDelay = randomUniform(0, 1000) // Any value 0-1000ms equally likely

// Seeded pseudo-random for reproducible tests
const seed = 42
const value1 = randomPseudo(seed) // Always same output for seed=42
const value2 = randomPseudo(seed) // Identical to value1

// UUID generation
const id = uuidV4() // "a3bb189e-8bf9-4558-9e3e-e7b9a9e7b8c1"
console.log(isUuidV4(id)) // true
console.log(isUuidV4('not-a-uuid')) // false

API Overview

Statistical Distributions

• randomUniform(min, max) - Uniform distribution (flat probability)
• randomGaussian(min, max) - Gaussian/normal distribution (bell curve)
• randomExponential(lambda) - Exponential distribution (decay)
• randomPowerLaw(alpha, min, max) - Power law distribution (long tail)
• randomLogarithmic(scale) - Logarithmic distribution

Pseudo-Random Generators

• randomPseudo(seed) - Seeded pseudo-random (reproducible)
• randomPseudoTimeBased(seedTime) - Time-based seeding for date/time variations

UUID Utilities

• uuidV4() - Generate RFC 4122 version 4 UUID
• isUuidV4(str) - Validate UUID v4 format

Use Cases

Load Testing

// Model realistic user behavior with varying response times
const users = Array.from({ length: 1000 }, () => ({
  thinkTime: randomExponential(0.5), // Time between actions
  responseTime: randomGaussian(50, 200), // Server response latency
  requestCount: Math.floor(randomPowerLaw(2, 1, 100)), // Request frequency
}))

Test Data Generation

// Generate reproducible test datasets
const seed = Date.now()
const testData = Array.from({ length: 50 }, (_, i) => ({
  id: uuidV4(),
  score: randomPseudo(seed + i) * 100, // Reproducible but varied
  timestamp: new Date(Date.now() + randomUniform(0, 86400000)),
}))

Procedural Content

// Generate varied but natural-looking values
const terrain = {
  height: randomGaussian(0, 100), // Centered around 50
  vegetation: randomUniform(0, 1), // Uniform coverage
  populationDensity: randomPowerLaw(2, 1, 1000), // Power law distribution
}

Compatibility

| Platform | Support | | ----------------------------- | :-----: | | Browser | ✅ | | Node.js | ✅ | | Web Workers | ✅ | | Deno, Bun, Cloudflare Workers | ✅ |

Output Formats

| Format | File | Tree-Shakeable | | ------ | -------------------------- | :------------: | | ESM | index.esm.js | ✅ | | CJS | index.cjs.js | ❌ | | IIFE | bundle/index.iife.min.js | ❌ | | UMD | bundle/index.umd.min.js | ❌ |

Bundle size: 1 KB (minified, self-contained)

CDN Usage

<!-- unpkg -->
<script src="https://unpkg.com/@hyperfrontend/random-generator-utils"></script>

<!-- jsDelivr -->
<script src="https://cdn.jsdelivr.net/npm/@hyperfrontend/random-generator-utils"></script>

<script>
  const { randomGaussian, randomUniform, uuid4 } = HyperfrontendRandomGenerator
</script>

Global variable: HyperfrontendRandomGenerator

Dependencies

| Package | Type | | ------------------------- | -------- | | @hyperfrontend/data-utils | Internal |

Part of hyperfrontend

This library is part of the hyperfrontend monorepo. Full documentation.

• Used by @hyperfrontend/cryptography for secure random generation

License

MIT